Q#1: Probabilistic reasoning over time deals with:
(A) Uncertainty in dynamic systems
(B) Static deterministic systems
(C) BFS nodes only
(D) DFS only
Answer: (A) Uncertainty in dynamic systems
Q#2: Hidden Markov Models (HMMs) are used for:
(A) Sequential probabilistic modeling
(B) Single-step deterministic reasoning
(C) BFS nodes only
(D) DFS only
Answer: (A) Sequential probabilistic modeling
Q#3: In HMMs, hidden states represent:
(A) True system states that are not directly observable
(B) Observed variables
(C) BFS nodes only
(D) DFS only
Answer: (A) True system states that are not directly observable
Q#4: Observations in HMMs are:
(A) Evidence dependent on hidden states
(B) Independent of hidden states
(C) BFS nodes only
(D) DFS only
Answer: (A) Evidence dependent on hidden states
Q#5: Transition model in HMMs represents:
(A) Probability of moving from one state to another
(B) Probability of evidence
(C) BFS nodes only
(D) DFS only
Answer: (A) Probability of moving from one state to another
Q#6: Observation model in HMMs represents:
(A) Probability of observation given hidden state
(B) Probability of next state only
(C) BFS nodes only
(D) DFS only
Answer: (A) Probability of observation given hidden state
Q#7: Filtering computes:
(A) Current belief state given all past observations
(B) Past state only
(C) BFS nodes only
(D) DFS only
Answer: (A) Current belief state given all past observations
Q#8: Prediction in HMMs computes:
(A) Future state probabilities
(B) Past state probabilities
(C) BFS nodes only
(D) DFS only
Answer: (A) Future state probabilities
Q#9: Smoothing in HMMs computes:
(A) Past state probabilities using all evidence
(B) Current state only
(C) BFS nodes only
(D) DFS only
Answer: (A) Past state probabilities using all evidence
Q#10: Viterbi algorithm finds:
(A) Most likely sequence of hidden states
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Most likely sequence of hidden states
Q#11: Dynamic Bayesian Networks (DBNs) generalize:
(A) HMMs to multiple variables per time step
(B) Single-variable HMMs only
(C) BFS nodes only
(D) DFS only
Answer: (A) HMMs to multiple variables per time step
Q#12: In DBNs, edges can represent:
(A) Dependencies within and between time slices
(B) Only past-to-future dependencies
(C) BFS nodes only
(D) DFS only
Answer: (A) Dependencies within and between time slices
Q#13: DBNs allow reasoning about:
(A) Complex temporal systems
(B) Static systems only
(C) BFS nodes only
(D) DFS only
Answer: (A) Complex temporal systems
Q#14: Exact inference in DBNs is often:
(A) Computationally expensive
(B) Trivial
(C) BFS nodes only
(D) DFS only
Answer: (A) Computationally expensive
Q#15: Approximate inference methods in DBNs include:
(A) Particle filtering
(B) Exact variable elimination
(C) BFS nodes only
(D) DFS only
Answer: (A) Particle filtering
Q#16: Particle filtering maintains:
(A) Set of weighted samples representing belief state
(B) Single deterministic state
(C) BFS nodes only
(D) DFS only
Answer: (A) Set of weighted samples representing belief state
Q#17: Particle filtering updates beliefs using:
(A) Transition model and observations
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Transition model and observations
Q#18: Temporal reasoning helps AI to:
(A) Make predictions and update beliefs over time
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Make predictions and update beliefs over time
Q#19: Sequential decision making under uncertainty uses:
(A) Partially Observable Markov Decision Processes (POMDPs)
(B) Classical deterministic planning only
(C) BFS nodes only
(D) DFS only
Answer: (A) Partially Observable Markov Decision Processes (POMDPs)
Q#20: Belief state in POMDPs represents:
(A) Probability distribution over possible states
(B) Single state only
(C) BFS nodes only
(D) DFS only
Answer: (A) Probability distribution over possible states
Q#21: Inference over time involves:
(A) Updating beliefs as new observations arrive
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Updating beliefs as new observations arrive
Q#22: Temporal probabilistic models assume:
(A) Markov property: next state depends only on current state
(B) Full history dependency
(C) BFS nodes only
(D) DFS only
Answer: (A) Markov property: next state depends only on current state
Q#23: Forward algorithm in HMMs is used for:
(A) Filtering
(B) Prediction only
(C) BFS nodes only
(D) DFS only
Answer: (A) Filtering
Q#24: Backward algorithm in HMMs is used for:
(A) Smoothing
(B) Filtering only
(C) BFS nodes only
(D) DFS only
Answer: (A) Smoothing
Q#25: Joint probability in temporal models represents:
(A) Probability over all states and observations across time
(B) Single time-step only
(C) BFS nodes only
(D) DFS only
Answer: (A) Probability over all states and observations across time
Q#26: Sequential probabilistic reasoning is useful for:
(A) Speech recognition, robot localization, tracking
(B) Static planning only
(C) BFS nodes only
(D) DFS only
Answer: (A) Speech recognition, robot localization, tracking
Q#27: Transition model defines:
(A) P(X_t | X_{t-1})
(B) P(O_t | X_t)
(C) BFS nodes only
(D) DFS only
Answer: (A) P(X_t | X_{t-1})
Q#28: Observation model defines:
(A) P(O_t | X_t)
(B) P(X_t | X_{t-1})
(C) BFS nodes only
(D) DFS only
Answer: (A) P(O_t | X_t)
Q#29: Temporal probabilistic models capture:
(A) Dynamics and uncertainty
(B) Static facts only
(C) BFS nodes only
(D) DFS only
Answer: (A) Dynamics and uncertainty
Q#30: Real-world applications include:
(A) Autonomous navigation and tracking
(B) Static scheduling only
(C) BFS nodes only
(D) DFS only
Answer: (A) Autonomous navigation and tracking
Q#31: Sequential inference often uses:
(A) Recursive updates
(B) Full joint distribution recomputation
(C) BFS nodes only
(D) DFS only
Answer: (A) Recursive updates
Q#32: Temporal reasoning can handle:
(A) Sensor noise and action uncertainty
(B) Perfect observations only
(C) BFS nodes only
(D) DFS only
Answer: (A) Sensor noise and action uncertainty
Q#33: POMDP solutions specify:
(A) Action for each belief state
(B) Single deterministic action
(C) BFS nodes only
(D) DFS only
Answer: (A) Action for each belief state
Q#34: Real-time sequential decision making uses:
(A) Online planning and execution
(B) Offline deterministic planning only
(C) BFS nodes only
(D) DFS only
Answer: (A) Online planning and execution
Q#35: Belief update in particle filtering involves:
(A) Weighting particles by observation likelihood
(B) Ignoring observations
(C) BFS nodes only
(D) DFS only
Answer: (A) Weighting particles by observation likelihood
Q#36: Resampling in particle filtering:
(A) Focuses on more probable particles
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Focuses on more probable particles
Q#37: Temporal probabilistic reasoning allows:
(A) Prediction, filtering, smoothing, and decision-making
(B) Deterministic reasoning only
(C) BFS nodes only
(D) DFS only
Answer: (A) Prediction, filtering, smoothing, and decision-making
Q#38: Temporal models assume:
(A) Current state depends only on previous state (Markov property)
(B) Full history dependency
(C) BFS nodes only
(D) DFS only
Answer: (A) Current state depends only on previous state (Markov property)
Q#39: Sequential decision making under uncertainty aims to:
(A) Maximize expected utility over time
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Maximize expected utility over time
Q#40: Temporal probabilistic models are crucial for:
(A) Robotics, speech, tracking, and prediction
(B) Static planning only
(C) BFS nodes only
(D) DFS only
Answer: (A) Robotics, speech, tracking, and prediction
Q#41: Dynamic Bayesian networks extend:
(A) HMMs to multiple interacting variables
(B) Single-variable HMMs only
(C) BFS nodes only
(D) DFS only
Answer: (A) HMMs to multiple interacting variables
Q#42: Sequential inference computes:
(A) Beliefs over states as time progresses
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Beliefs over states as time progresses
Q#43: Real-world temporal reasoning handles:
(A) Noisy sensors, stochastic actions, partial observability
(B) Deterministic actions only
(C) BFS nodes only
(D) DFS only
Answer: (A) Noisy sensors, stochastic actions, partial observability
Q#44: Online temporal reasoning allows:
(A) Updating beliefs and selecting actions as evidence arrives
(B) Offline plan execution only
(C) BFS nodes only
(D) DFS only
Answer: (A) Updating beliefs and selecting actions as evidence arrives
Q#45: Sequential decision problems are often:
(A) NP-hard
(B) Trivial
(C) BFS nodes only
(D) DFS only
Answer: (A) NP-hard
Q#46: Approximate methods help:
(A) Scale sequential probabilistic reasoning to real-world problems
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Scale sequential probabilistic reasoning to real-world problems
Q#47: Real-time applications require:
(A) Fast belief updates and action selection
(B) Offline batch computation only
(C) BFS nodes only
(D) DFS only
Answer: (A) Fast belief updates and action selection
Q#48: Temporal probabilistic reasoning combines:
(A) Probabilistic inference and sequential modeling
(B) Deterministic reasoning only
(C) BFS nodes only
(D) DFS only
Answer: (A) Probabilistic inference and sequential modeling
Q#49: Particle filtering approximates:
(A) Belief states in high-dimensional dynamic systems
(B) Exact joint probability
(C) BFS nodes only
(D) DFS only
Answer: (A) Belief states in high-dimensional dynamic systems
Q#50: The main goal of probabilistic reasoning over time is:
(A) Make predictions, update beliefs, and choose actions under uncertainty
(B) BFS nodes only
(C) DFS only
(D) Random assignments
Answer: (A) Make predictions, update beliefs, and choose actions under uncertainty